@misc{bose2021understanding,
title = {Understanding of Emotion Perception from Art},
author = {Digbalay Bose and Krishna Somandepalli and Souvik Kundu and Rimita Lahiri and Jonathan Gratch and Shrikanth Narayanan},
year = {2021},
eprint = {2110.06486},
archiveprefix = {arXiv},
abstract={Computational modeling of the emotions evoked by art in humans is a challenging problem because of the subjective and nuanced nature of art and affective signals.
In this paper, we consider the above-mentioned problem of understanding emotions evoked in viewers by artwork using both text and visual modalities.
Specifically, we analyze images and the accompanying text captions from the viewers expressing emotions as a multimodal classification task.
Our results show that single-stream multimodal transformer-based models like MMBT and VisualBERT perform better compared to both image-only models and dual-stream multimodal models having separate pathways for text and image modalities.},
keywords = {Affective Computing, Multimodal classification, Emotion Recognition},
primaryclass = {cs.CV}
}
@misc{olah2021cross,
title = {Cross Domain Emotion Recognition using Few Shot Knowledge Transfer},
author = {Justin Olah and Sabyasachee Baruah and Digbalay Bose and Shrikanth Narayanan},
year = {2021},
eprint = {2110.05021},
archiveprefix = {arXiv},
abstract={Emotion recognition from text is a challenging task due to diverse
emotion taxonomies, lack of reliable labeled data in different domains, and highly subjective annotation standards. Few-shot and
zero-shot techniques can generalize across unseen emotions by projecting the documents and emotion labels onto a shared embedding
space. In this work, we explore the task of few-shot emotion recognition by transferring the knowledge gained from supervision on
the GoEmotions Reddit dataset to the SemEval tweets corpus, using
different emotion representation methods. The results show that
knowledge transfer using external knowledge bases and fine-tuned
encoders perform comparably as supervised baselines, requiring
minimal supervision from the task dataset.},
keywords={Emotion Recognition, Few Shot classification, Unsupervised methods},
primaryclass = {cs.CL}
}
@inproceedings{10.1145/3430984.3431003,
author = {Mukherjee, Sumanta and Narayanam, Krishnasuri and Aggarwal, Nupur and Bose, Digbalay and Singhee, Amith},
title = {Robust Resource Demand Estimation Using Hierarchical Bayesian Model in a Distributed Service System},
year = {2021},
isbn = {9781450388177},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3430984.3431003},
doi = {10.1145/3430984.3431003},
abstract = { Robust resource demand prediction is crucial for efficient allocation of resources
to service requests in a distributed service delivery system. There are two problems
in resource demand prediction: firstly to estimate the volume of service requests
that come in at different time points and at different geo-locations, secondly to
estimate the resource demand given the estimated volume of service requests. While
a lot of literature exists to address the first problem, in this work, we have proposed
a data-driven statistical method for robust resource demand prediction to address
the second problem. The method automates the identification of various system operational
characteristics and contributing factors that influence the system behavior to generate
an adaptive low variance resource demand prediction model. Factors can be either continuous
or categorical in nature. The method assumes that each service request resolution
involves multiple tasks. Each task is composed of multiple activities. Each task belongs
to a task type, based on the type of the resource it requires to resolve that task.
Our method supports configurable tasks per service request, and configurable activities
per task. The demand prediction model produces an aggregated resource demand required
to resolve all the activities under a task by activity sequence modeling; and aggregated
resource demand by resource type, required to resolve all the activities under a service
request by task sequence modeling.},
booktitle = {8th ACM IKDD CODS and 26th COMAD},
pages = {350–358},
numpages = {9},
keywords = {factor analysis, hierarchical Bayesian model, robust estimation, Distributed service delivery system},
location = {Bangalore, India},
series = {CODS COMAD 2021}
}
@inproceedings{10.1145/3009977.3010064,
author = {Bose, Digbalay and Chaudhuri, Subhasis},
title = {Hierarchical Spectral Clustering Based Large Margin Classification of Visually Correlated Categories},
year = {2016},
isbn = {9781450347532},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3009977.3010064},
doi = {10.1145/3009977.3010064},
abstract = {Object recognition is one of the challenging tasks in computer vision and the problem
becomes increasingly difficult when the image categories are visually correlated among
themselves i.e. they are visually similar and only fine differences exist among the
categories. This paper has a two-fold objective which involves organization of the
image categories in a hierarchical tree like structure using self tuning spectral
clustering for exploiting the correlations among them. The organization phase is followed
by a node specific large margin nearest neighbor classification scheme, where a Mahalnobis
distance metric is learnt for each non-leaf node. Further a procedure for hyperparameters
selection has been discussed w.r.t two strategies i.e. grid search and Bayesian optimization.
The proposed algorithm's effectiveness is tested on selected classes of the popular
Imagenet dataset.},
booktitle = {Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing},
articleno = {48},
numpages = {8},
keywords = {large margin nearest neighbor classification, hierarchical organization, visually correlated categories, self tuning spectral clustering, object recognition},
location = {Guwahati, Assam, India},
series = {ICVGIP '16}
}
@article{10.1016/j.ins.2014.05.033,
author = {Bose, Digbalay and Biswas, Subhodip and Vasilakos, Athanasios V. and Laha, Sougata},
title = {Optimal Filter Design Using an Improved Artificial Bee Colony Algorithm},
year = {2014},
issue_date = {October, 2014},
publisher = {Elsevier Science Inc.},
address = {USA},
volume = {281},
issn = {0020-0255},
url = {https://doi.org/10.1016/j.ins.2014.05.033},
doi = {10.1016/j.ins.2014.05.033},
abstract = {The domain of analog filter design revolves around the selection of proper values
of the circuit components from a possible set of values manufactured keeping in mind
the associated cost overhead. Normal design procedures result in a set of values for
the discrete components that do not match with the preferred set of values. This results
in the selection of approximated values that cause error in the associated design
process. An optimal solution to the design problem would include selection of the
best possible set of components from the numerous possible combinations. The search
procedure for such an optimal solution necessitates the usage of Evolutionary Computation
(EC) as a potential tool for determining the best possible set of circuit components.
Recently algorithms based on Swarm Intelligence (SI) have gained prominence due to
the underlying focus on collective intelligent behavior. In this paper a novel hybrid
variant of a swarm-based metaheuristics called Artificial Bee Colony (ABC) algorithm
is proposed and shall be referred to as CRbABC_Dt (Collective Resource-based ABC with
Decentralized tasking) and it incorporates the idea of decentralization of attraction
from super-fit members along with neighborhood information and wider exploration of
search space. Two separate filter design instances have been tested using CRbABC_Dt
algorithm and the results obtained are compared with several competitive state-of-the-art
optimizing algorithms. All the components considered in the design are selected from
standard series and the resulting deviation from the idealized design procedure has
been investigated. Additional empirical experimentation has also been included based
on the benchmarking problems proposed for the CEC 2013 Special Session & Competition
on Real-Parameter Single Objective Optimization.},
journal = {Inf. Sci.},
month = oct,
pages = {443–461},
numpages = {19},
keywords = {Global optimization, Analog filter design, Passive circuit component, Information sharing, Swarm intelligence, Artificial bee colony algorithm}
}
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